spf-implement-feature

name

spf-implement-feature

description

Implement a feature documented in the SPF feature registry. Consumes a feature doc at internal/design/spf/features/<name>.md and produces the engine-side code: new behaviors, updates to existing behaviors, media-layer / network-layer primitives, and tests. The implementation analog of /spf-document-feature (which produces the doc; this consumes it). Walks through resolving the doc's open questions before coding, maps phases to discrete chunks, applies the SPF conventions catalog, routes to downstream skills (/spf-create-behavior, /spf-update-behavior, /refactor-behavior) per chunk shape, and updates the feature doc's Status / Implementation surface / Verification sections as code lands. Triggers: "implement feature", "implement SPF feature", "build feature", "code feature", "scope feature implementation", "implement <feature-name>".

Implement an SPF Feature

Take a feature documented at internal/design/spf/features/<name>.md and produce the engine-side code that satisfies its Phase 1 (and optionally Phase 2 / Phase 3) scope. The canonical failure mode without this discipline is jumping from "implement audio-abr" to writing code — producing implementation that doesn't match the doc's grounding, drifts beyond the agreed phase scope, misses the conventions catalog, or skips the doc-update cascade that keeps the feature registry current as code lands.

Steps 1–3 are the load-bearing setup. Skipping them produces implementations that look superficially correct but anchor on the wrong scope, miss the doc's open questions, miss adjacent features whose shape constrains this one, or fail to coordinate with cross-cutting concerns. Steps 4–8 only make sense once the feature, composition target, fold-ins, and intent are named. Step 9 (doc update as living artifact) is where the registry stays in sync with code.

Usage

/spf-implement-feature [<feature-name>]

The arg is the feature doc's filename (without extension), e.g. audio-abr. The skill reads the feature doc as its source of truth.

Doc-as-starting-point principle

The feature doc is the starting point for planning, not a hardened specification. This principle is load-bearing throughout the skill — explicit because both directions of failure are real:

• Silent override (covered by Feature-doc-grounding drift failure mode) — implementation diverges from the doc without surfacing the divergence; the doc becomes stale silently.
• Rigid following (covered by Treating feature doc as hardened spec failure mode) — implementation refuses to revise the doc when new questions surface or drift is discovered; the doc becomes a misleading constraint.

Acknowledge the doc's definition depth explicitly when planning:

• coarse — feature sketched, many open questions. Implementation fills in significant detail; revisions to the doc are expected throughout. Planning is substantial.
• technical — scope and constraints articulated; specifics still open. Implementation maps to constraints; moderate planning + revision.
• sketched — implementation surface populated. Implementation primarily verifies; revisions for drift only.

Discipline:

• Every doc revision is explicit and surfaced to the user during the implementation pass. Never silent.
• Step 9 consolidates the cumulative doc update reflecting all revisions made during implementation — it's not the only revision point, just the cumulative one.
• Open questions are markers, not absent specs — the implementation resolves them through experience or explicitly defers them.

Reference docs

Primary:

• internal/design/spf/features/<name>.md — source of truth for what to implement. Read end-to-end before scoping.
• internal/design/spf/conventions/*.md — when to reach for which primitive (behaviors, signals, reactors, actors, config). Applied throughout implementation.
• internal/design/spf/evaluation-axes.md — A–E axes the implementation is scored against. Cleanup pass and feature work share the same axes.
• packages/spf/docs/hls-engine.md — current HLS engine composition walkthrough; the baseline the implementation extends.
• packages/spf/src/CLAUDE.md — source layout + dependency rules.

Secondary:

• internal/design/spf/use-cases/<name>.md — if implementing for a use-case-specific path (variant composition).
• internal/decisions/*.md — past tactical decisions constraining shape.
• internal/design/spf/architecture.md, primitives.md, signals.md — implementation-level "how it works."
• Existing similar behaviors / actors as templates (e.g., switchVideoQuality as a template for switchAudioQuality when implementing audio-abr).

Downstream skills routed-to:

• .claude/skills/spf-create-behavior/SKILL.md — new behaviors.
• .claude/skills/spf-update-behavior/SKILL.md — existing behaviors whose purpose is changing.
• .claude/skills/refactor-behavior/SKILL.md — existing behaviors whose purpose is preserved but implementation improves.
• .claude/skills/split-behavior/SKILL.md, .claude/skills/merge-behaviors/SKILL.md — structural changes.
• (future) media-layer / network-layer skills for packages/spf/src/media/ and packages/spf/src/network/ changes.

Failure-mode catalog (seeded; grows with use)

• Skipping disambiguation — invoking with a name or description that ambiguously maps to (i) an existing feature, (ii) a different feature, (iii) a use case, (iv) cluster-E policy, (v) something not yet documented. Step 1's disambiguation must resolve before gathering sources or planning. Proceeding-with-assumption is the canonical failure shape. Worked example: invocation "implement resolution capping" — could be (i) the rendition-selection-caps feature (the right route; cluster-E selection policy), (ii) a use-case composition shape (incorrect — composition vs runtime config), or (iii) something else entirely. The disambiguation discipline catches it. Another worked example: invocation "implement audio-only" maps to the audio-only-mode-override use case (which absorbed what was previously framed as a separate audio-only-composition feature); the disambiguation is between use case and cluster-E policy, not between feature framings.

• Routing-out failure — Step 1's disambiguation should route confidently. The failure mode is "ambiguity discovered but not resolved" — silently picking one interpretation when the user could have meant another (especially: a request that's actually a use case routed-here as a feature, or vice versa). Always surface and confirm.

• Standard-vs-use-case composition routing skipped — Step 1 must confirm whether the implementation effort is for the base/standard composition or for a specific use-case composition. Default is base, but use-case-FOR implementations have different framing: the use-case doc supplies the destination-architecture sketch; cluster traversal narrows. Worked example: implementing multi-language-audio for the base engine forces the cluster's destination-architecture frame; implementing it as part of audio-only-mode-override Phase 2 narrows to the use case's variant engine.

• Destination architecture unframed — for features in the base composition, scoping without sketching the cluster's destination architecture produces local-optimal cuts that don't fit the larger shape. The destination sketch is Step 2's reference frame for the fold-in assessment; without it, the assessment defaults to "is this candidate adjacent to my feature?" rather than "does my feature need to align with where this cluster is heading?"

• Doc-related-features-walked-without-cluster-traversal — relying solely on the feature doc's flat Related features list misses architectural-axis siblings reachable via clusters.md (primary cluster siblings + cross-cluster axis siblings). The Related features list is a starting point; the cluster traversal is what surfaces the architectural axis. Worked example: multi-language-audio's Related features list includes capability-probing as a "candidate," but doesn't loudly surface that capability-probing sits on the same "filtering/prioritizing/selecting tracks of a given type" axis — cluster traversal makes that axis explicit and the fold-in candidacy clearer.

• Cross-cluster axis traversal short-circuit — treating the feature doc's Related features list as the ceiling for cross-cluster candidates rather than the floor. Has two faces: (i) surfacing failure — not enumerating the cross-cluster cousins at all; (ii) assessment failure — enumerating them but rating them all "ignore for now," skipping past the design-with-in-mind landing zone.

  For cluster-C (track & variant registry) features, at minimum check capability-probing (D), rendition-selection-caps (E), multi-cdn-failover (G), drm-support (H) — these cousins all participate in the "filter / prioritize / select track candidates" axis (see clusters.md → Selection / filtering across clusters). Analogous cross-cluster cousins exist for other primary clusters; consult the clusters doc before defaulting to "no cross-cluster candidates."

  DWIM is the default recommendation for cross-cluster cousins, unless Impact-if-deferred pushes higher or Shape-constraint-if- deferred is genuinely low. The clusters doc names this explicitly: "Candidates often land as design-with-in-mind rather than full fold-in (each cluster owns its own primitives), but surfacing them keeps the cluster-C feature's shape from painting into a corner." See Step 2d's Default-recommended outcome per candidate type table — cross-cluster cousins are the row where DWIM is the prior, not Ignore.

  Worked example: multi-language-audio's Related features lists capability-probing but omits rendition-selection-caps, multi-cdn-failover, drm-support. The implementation pass should rate all four as DWIM: codec filtering composes with the userAudioTrackSelection filter slot on the same axis; audio caps would bias the same candidate set the filter narrows; per-language URI rotation needs to stay extensible during mid-stream-switch design; per-language key-system filtering interacts with selection. Rating any of these "ignore" without surfacing as DWIM is the assessment-failure shape; rating all four as DWIM without surfacing each via AskUserQuestion is the narrative-batched-skip shape (see next entry).

• Narrative-batched-skip of AskUserQuestion for "obvious" candidates — collapsing multiple fold-in candidates into a single narrative sentence ("the remaining cross-cluster cousins all default to ignore for now") and skipping the per-candidate AskUserQuestion because the recommendation feels obvious. The structured presentation is the load-bearing pressure that surfaces assessment misjudgments — bypassing it lets misjudged recommendations slip through unchallenged. This failure mode often pairs with Cross-cluster axis traversal short-circuit's assessment-failure face: candidates rated "ignore for now" feel uncontroversial enough to batch, and the batching is what hides the misjudgment.

  Discipline. Use AskUserQuestion per candidate regardless of recommendation strength, even when the recommendation feels obvious. If multiple candidates would otherwise be batched into one narrative paragraph, group by shared rationale into 2–3 AskUserQuestion calls (4 candidates per call, per the AskUserQuestion cap) — but every candidate appears as a discrete row with discrete options, with DWIM and Ignore both visible in the option list for cross-cluster cousins specifically.

  Worked example: implementation pass enumerated 4 cross-cluster cousins (cap-probing / caps / CDN / DRM) and assessed all as "ignore for now," then narrative-batched ("the cross-cluster cousins all default to ignore"). User pushback identified 3 of 4 as design-with-in-mind that the narrative-batch silently suppressed. Per-candidate AskUserQuestion with DWIM visible would have caught the misjudgment by the first candidate.

• Order-inversion not surfaced — when this feature anchors on a sibling's slot-owner shape (a destination-architecture sibling), implementing the sibling first may be the right route. This is a legitimate fold-in outcome distinct from full / partial / design-with-in-mind / ignore — surface it explicitly to the user, don't silently reject as "we're implementing this feature, not that one." Signal: a sibling at definition: technical (or deeper) carries the destination architecture in its Phases of complexity table. Worked example: multi-language-audio anchors on switchAudioQuality (audio-abr Phase 3 + Phase 4 + selectedAudioTrackId triple-writer characterization in Phase 5). Implementing multi-language-audio while extending the current selectAudioTrack shape — instead of introducing switchAudioQuality per audio-abr's destination — would unwind when audio-abr ships. The "audio-abr first" ordering must be surfaced as an option before the fold-in walk, not buried inside one candidate's per-question outcomes.

• Boil-the-ocean false positive — rejecting fold-in candidates reflexively as "too big" without considering partial-implementation. The right question is "is there a focused partial-implementation cut?" not "is the full feature big?" Worked example: rejecting audio-abr as boil-the-ocean when a basic switchAudioQuality shape that establishes placement + slot pattern is a focused partial that gets audio-ABR's foundation in place without implementing the full feature.

• Conflating partial-implementation with design-with-in-mind — these are distinct fold-in outcomes. Partial = concrete code lands; design-with-in-mind = no new code, but this feature's shape is constrained by the candidate's eventual needs. Treating them as one outcome loses the distinction and produces either too little code (skipping high-impact partials) or too much (writing speculative code for design considerations). Worked example: audio-abr is partial-implementation (basic switchAudioQuality); 5.1-surround-selection is design-with-in-mind (no codec-change code, but mid-stream flush orchestration left extensible).

• Value-curve check skipped — scoping to "spec-compliant baseline" without checking whether the cut produces user-meaningful capability. Per clusters.md's "can-play vs actual support" distinction, a cut that ships only passive recognition / spec-compliance is a value-curve red flag — either fold in more, or be explicit with the user about the constraint. Worked example: multi-language-audio Tier 1 alone produces "the engine recognizes audio tracks and picks one at load time" — still only "can-play" at the cluster's value-curve, since the consumer can't dynamically select among them.

• Treating feature doc as hardened spec — refusing to revise the doc when implementation reveals new questions, refines framing, or surfaces drift. Inverse failure of silent-override (see Feature-doc-grounding drift). The right discipline is explicit, user-surfaced revision per the Doc-as-starting-point principle section above.

• Skipping the open-questions discussion — feature docs at coarse depth have unresolved open questions that block implementation. Resolving them silently via the edit loses the user's design intent. Always discuss before coding. Worked example: audio-abr's "Audio caps" open question needs resolution before Phase 1 — does the implementation honor a max- audio-bitrate cap as a constraint+filter, or is it deferred to Phase 2?

• Implementing beyond the agreed phase scope — feature docs have multiple phases. The skill must explicitly scope to one or a subset, not "implement the whole feature." Worked example: audio-abr Phase 1 is "BandwidthState reuse + switchAudioQuality behavior parallel to switchVideoQuality"; landing Phase 2 (multi-signal extensions) at the same time is scope creep.

• Conventions catalog under-application — SPF conventions (behaviors, signals, reactors, actors, config) all apply during implementation. Failing to consult them produces code that "works" but doesn't match patterns, requiring later refactor.

• Feature-doc-grounding drift — implementing something that doesn't match what the feature doc said. Either the doc is wrong (update it) or the implementation is wrong (fix it). Don't silently diverge — the doc is the spec, and divergence either way invalidates it.

• Cross-cutting impact under-checked — the doc's Likely cross-cutting impact section flags decisions the implementation forces elsewhere. Skip → cascading-impact misses. Worked example: implementing audio-abr without verifying bandwidthState multi-writer characterization (audio samples + video samples both writing) misses the EWMA-mixed-source concern the doc flags.

• Multi-writer slot mishandling — adding a writer to a slot another behavior already writes requires multi-writer characterization (per conventions/signals.md). Default-merge or default-overwrite without coordination is a bug.

• Test-after-the-fact implementation — TDD discipline per chunk: test → implement → verify. Implementation-first produces tests that pass by construction.

• Composing into wrong engine factory — variant-specific behaviors compose into the variant factory (per use-cases/); the default factory stays clean. Cross-ref to use-cases/README composition discipline. If a use case has a Case-2-only behavior, it doesn't belong in createSimpleHlsEngine.

• Status / Implementation-surface update skipped — feature doc transitions from coarse → sketched as code lands. The doc must be updated as part of the implementation; deferring means future agents see stale status. Step 9 enforces.

• Downstream skill missing — silent inline implementation — when a chunk hits a "Yes" row in the downstream-skill-needed table (new behavior, non-trivial behavior update), and the downstream skill doesn't exist yet, the failure mode is to silently apply discipline ad-hoc. Step 7 explicitly surfaces this: branch on (i) defer chunk pending downstream skill, (ii) build the downstream skill inline now, (iii) apply discipline ad-hoc with explicit "extract later" flag.

Steps (do these in order; do not skip)

Step 1 — Identify the feature + disambiguate the request

The load-bearing setup step. Disambiguation comes first — before gathering sources or planning, confirm what the user actually wants.

1a. Identify the candidate.

• If the user passed a name → verify internal/design/spf/features/<name>.md exists.
• If the user passed a description (no name) → parse for candidates, match against existing feature docs.
• If multiple candidates match → surface options to the user.

1b. Verify the candidate is actually a feature.

Apply the discriminator from features/clusters.md and the use-cases boundary:

• Source-shape correctness or engine capability? If yes → feature (Case-1), stay here.
• Delivery-mode choice / variant assembly? If yes → this is a use-case composition (Case-2) → route to /spf-implement-use-case.
• Runtime policy tuning without composition change? If yes → still a feature (cluster-E policy), but the implementation shape is config/middle-pattern, not composition; stay here.
• Ambiguous between Case-1 and Case-2? Same vocabulary often appears on both sides (audio-only, video-only, live). Surface to user; don't pick silently.

1c. Route the request appropriately.

• Stays here — confirmed feature, doc exists. Proceed to 1d.
• No doc exists for the candidate → route to /spf-document-feature to produce the doc; return here once doc lands.
• It's actually a use case, doc exists → route to /spf-implement-use-case.
• It's actually a use case, no doc exists → route to /spf-document-use-case first, then /spf-implement-use-case.
• Ambiguous between options → surface to user; do not pick silently.

1d. Confirm composition target. (Once routing is confirmed.)

The implementation effort's composition target shapes scope considerably. Default is standard composition (createSimpleHlsEngine); use-case compositions (createHlsAudioOnlyEngine, etc.) follow a narrower frame.

If not explicit in the invocation, surface and confirm. Three answers:

• Standard composition (default) — feature lands in createSimpleHlsEngine (or its constituents). Step 2's destination-architecture frame applies in full.
• Use-case composition: <use-case-name> — feature lands in the use case's variant engine. Step 2's destination-architecture frame is supplied by the use-case doc; fold-in traversal narrows.
• Both — feature lands in standard composition; one or more use-case compositions may need refactoring to absorb it. Treat each use-case refactor as a fold-in candidate in Step 2.

If the user didn't specify, default to standard composition and flag the assumption in the Step 1 report so the user can correct.

1e. Gather sources. (Once routing and composition target are confirmed.)

Triangulate from every available source:

• The user's invocation message. Feature name? Phase scope target? Specific concerns?
• The feature doc itself. Read end-to-end. Pay attention to: Status block (what's already there?), Phases of complexity (what scope is available?), Open questions (what blocks implementation?), Likely cross-cutting impact (what will the implementation force elsewhere?), Implementation surface / What's not implemented (where in code?). Note the doc's definition depth and status per the Doc-as- starting-point principle above.
• Constituent feature docs. Per the use-cases/README.md framing, features may compose into use cases. If implementing for a use case, read the use-case doc to understand the composition target.
• Similar implementations as templates. Identify analog behaviors in the codebase. Worked example: implementing switchAudioQuality benefits massively from reading switchVideoQuality first — same shape, audio-axis.
• Conventions catalog. Skim conventions/*.md for relevance signals.
• Recent ADRs. Check internal/decisions/ for tactical decisions that constrain the implementation shape.

Stop and report back to the user with:

1. The feature name.
2. The confirmed composition target (standard / use-case / both).
3. Sources consulted (with links).
4. Feature doc status — what's already there vs what needs implementing.
5. Likely template implementations (similar behaviors / actors / helpers).
6. The feature's phases (from the doc) — surfaced for context, with the phase-scope decision deferred to Step 3 once fold-in assessment in Step 2 has shaped it.

Step 2 — Frame the work: destination architecture + fold-in assessment

Bridges "this feature, alone" and "this feature, as one slice of a larger architectural concern." Without it, scope-narrow choices that look reasonable in isolation produce implementations that don't fit the destination architecture, get reworked when adjacent features land, or ship capabilities of low user-meaningful value.

Five movements (2a–2e). All five are required for features in the standard composition; for features being implemented FOR a specific use-case composition (per Step 1d), 2b lightens considerably — the use-case doc supplies the destination-architecture frame.

2a. Identify the architectural axis / cluster.

Per features/clusters.md, the feature lives in one or more clusters and may also sit on a cross-cluster axis (e.g., "selecting / filtering / prioritizing tracks of a given type"). Both matter:

• Primary cluster — the section in clusters.md the feature belongs to. Gives the direct siblings.
• Cross-cluster axis — abstractions shared with features in other clusters. The clusters doc's Cross-cluster patterns section captures some of these (multi-writer state slots, constraint + filter, per-type specialization, sampling-baked-into-loading). The axis the feature sits on is often inferable from the feature's verb-or-noun-phrase: "select" / "filter" / "prioritize" / "sample" / "load" / "recover" / "track-selection-for-type-X."

Surface both: "this feature is in cluster X; it sits on the [axis-name] axis alongside features in clusters X, Y, Z."

2b. Sketch the destination architecture.

For features being implemented for the standard composition (the default), sketch where the cluster's destination architecture is heading — the larger shape this feature is one slice of. The sketch is the reference frame for the fold-in assessment.

Format: a few sentences naming (i) the shape (multi-writer slot, constraint + filter, per-type sibling triad, etc.), (ii) the moving pieces (slots, behaviors, primitives), (iii) the open questions about where things live (the cluster's known unknowns). The sketch is necessarily approximate — its job is to give planning the right frame, not commit to design.

Mine sibling docs for already-articulated destination shapes. Before sketching from scratch, scan cluster siblings' feature docs for an already-articulated destination shape. A sibling at definition: technical (or deeper) often carries the destination architecture in its Phases of complexity table — behavior names, slot ownership, multi-writer characterization. If found, the sketch becomes a cross-reference to that sibling's articulated shape rather than a fresh draft. When the destination sibling is itself unimplemented (e.g., audio-abr's switchAudioQuality for multi-language-audio), this triggers an ordering question — surface it to the user in Step 2's report, before the per-candidate fold-in walk, per the Order-inversion not surfaced failure mode.

For features being implemented FOR a use-case composition, the use-case doc supplies most of this frame; cross-reference rather than re-sketch.

2c. Enumerate fold-in candidates.

Walk three sources of candidates:

• Cluster siblings (from 2a's primary cluster).
• Cross-cluster axis siblings (from 2a's axis — features in other clusters that share the abstraction). For cluster-C (registry) features, at minimum check capability-probing (D), rendition-selection-caps (E), multi-cdn-failover (G), drm-support (H) regardless of whether the feature doc lists them — see clusters.md → Selection / filtering across clusters. Analogous cross-cluster cousins exist for other primary clusters; consult that section before defaulting to "no cross-cluster candidates."
• The feature doc's Related features list (catches anything the cluster traversal missed — but treat the list as the floor, not the ceiling).

Candidates can be (i) whole features, (ii) specific phases of features, or (iii) use-case-composition refactors (a minor refactor to a use case's variant engine to absorb this feature's new behaviors counts as a fold-in candidate).

2d. Assess each candidate; recommend an outcome.

Four criteria per candidate. Shape-constraint-if-deferred comes first because it's the criterion that catches design-with-in-mind — the other three are oriented toward "should we write code for this candidate now?" and miss the design-shape question if it's not asked separately. Per the Cross-cluster axis traversal short-circuit failure mode.

1. Shape-constraint-if-deferred — if we don't design this feature with the candidate's eventual shape in mind, will the structures we land make later integration awkward? Will slots, composition seams, layering decisions, or filter-pipeline order need to mutate when the candidate ships? High shape-constraint → design-with-in-mind (or stronger); low shape-constraint → ignore is safe.
2. Impact if deferred — would solving this feature without considering the candidate produce a solution that needs significant refactor / rework / re-characterization when the candidate later lands? Or that would be insufficient when the candidate lands?
3. Overall speed if combined — would partially or fully implementing the candidate now produce overall less work than serial implementation, even at moderate cost to this feature's specific velocity?
4. Scope discipline — is the candidate's bundled work focused enough to remain a discrete partial-implementation, or does it sprawl into ocean-boiling territory?

The fourth criterion is where the boiling-the-ocean check lives — but it's a check on scope sprawl, not a default veto. Per the Boil-the-ocean false positive failure mode, rejecting candidates reflexively as "too big" misses the real question: "can this be cut to a focused partial-implementation?"

Why four criteria, not three. The previous three-criteria rubric biased the recommendation toward "implement now or ignore" — when the implement-now threshold failed, the natural cognitive landing slid past design-with-in-mind to ignore. Lifting shape-constraint to criterion #1 pulls the design-shape question to the front of the assessment, where it can land on DWIM as the right answer instead of being skipped past.

Five outcomes per candidate:

• Implement candidate first (flip ordering) — when the candidate carries this feature's destination architecture (per 2b), implementing it first is the right route. This feature exits the current implementation pass and returns later, anchored on the candidate's slot-owner shape. Applies only to destination- architecture siblings; for ordinary fold-in candidates, this outcome doesn't apply. Per the Order-inversion not surfaced failure mode.
• Fold in (full) — implement the candidate (or candidate phase) in this implementation pass. Used when impact is high and scope fits.
• Partial implementation — implement a focused subset of the candidate in this pass. Concrete code lands; some of the candidate stays unimplemented. Used when impact is high and a focused cut exists.
• Design with in mind — no new code for the candidate, but this feature's implementation shape is constrained by the candidate's eventual needs. Used when impact is medium and the candidate's shape is enough to inform design but not enough to warrant code.
• Ignore for now — defer entirely. Used when impact is low or the candidate's eventual landing wouldn't meaningfully constrain this feature's shape.

Per the Conflating partial-implementation with design-with-in-mind failure mode, the partial vs design-with-in-mind distinction is load-bearing — partial means code lands; design-with-in-mind means no code, only shape constraints.

Default-recommended outcome per candidate type. The four-criteria walk can land anywhere on the outcome spectrum, but candidate type carries strong priors. Diverging from the default without naming which criterion pushed the assessment is a smell:

Candidate type	Default outcome	Notes
Destination-architecture sibling (cluster sibling at definition: technical+ carrying this feature's destination slot-owner shape)	Flip ordering or Full fold-in	Per Order-inversion not surfaced failure mode. Never ignore — architecturally wrong by construction.
Primary cluster sibling	Full fold-in / Partial	Assess via the four criteria. Ignore only if low-impact AND low-shape-constraint.
Cross-cluster axis cousin (e.g., cluster D / E / G / H cousins for a cluster-C feature)	Design with in mind	Per Cross-cluster axis traversal short-circuit failure mode + clusters.md → Selection / filtering across clusters. Promote to Partial / Full only if Impact-if-deferred clears the threshold; demote to Ignore only if Shape-constraint-if-deferred is genuinely low.
Use-case composition refactor	Design with in mind	Let standard composition land cleanly; verify the variant engine composes post-landing.

The defaults are starting points. When your assessment lands at a different outcome, name which criterion pushed it (e.g., "Impact-if-deferred is high, so promoting capability-probing from DWIM to Partial"). The defaults exist to counter-bias the historical "implement now / ignore" pull of the four-criteria walk — particularly for cross-cluster cousins, where the cluster doc already says DWIM is the typical landing.

Value-curve check before recommending scope. Per clusters.md's "can-play vs actual support" framing, check whether the scope-as-recommended produces user-meaningful capability. A cut that ships only spec-compliance / passive recognition is a signal that the scope needs revisiting — either fold in more of the dynamic / interactive phases, or be explicit with the user about the value-curve constraint.

2e. Ask the user to confirm — one question per candidate.

Use AskUserQuestion to present each candidate's recommendation and let the user confirm or override. One question per candidate; batch into rounds of up to 4 per AskUserQuestion call. For each candidate's question:

• Question text — name the candidate and summarize the assessment in one sentence.
• Options (4) — four of the outcomes, with the recommended outcome listed first and labeled "(Recommended)". Order the remaining three by the next-best fit per the assessment.
• Description per option — one-line summary of what that outcome means for this candidate (concrete: "implement basic switchAudioQuality alongside" / "leave flush orchestration codec-change-extensible" / "no change").

When the candidate carries destination architecture (per 2b), the five-outcome list exceeds AskUserQuestion's 4-option cap. Drop "Ignore for now" — ignoring a destination-architecture sibling is architecturally-wrong-by-construction. The remaining four (Flip ordering / Full fold-in / Partial / Design-with-in-mind) fit the cap. Ordering surfacing also belongs in Step 2's narrative report, not just inside the per-candidate AskUserQuestion: mention the destination-architecture sibling and the flip-ordering option in prose before the AskUserQuestion call, so the user sees the framing alongside the recommendation.

Allow the user to select Other for adjustments not captured by the listed-outcome shape (e.g., "partially implement but limit to X").

After confirmation, the agreed scope is the union of:

• This feature's chosen phases (refined in Step 3).
• Each fold-in / partial-implementation candidate's chosen scope.
• Each "design with in mind" candidate's shape constraint (informs design without adding code).

This agreed scope feeds Step 3's open-questions discussion and final scope confirmation.

Stop and report back to the user with:

1. The cluster + axis identification (from 2a).
2. The destination architecture sketch (from 2b).
3. The candidate enumeration + per-candidate assessment + recommended outcome (from 2c-2d).
4. The value-curve check result.
5. The AskUserQuestion-confirmed outcomes per candidate (from 2e).
6. The resulting agreed scope.

Step 3 — Resolve open questions + confirm final scope

An explicit conversational stage — not optional, not implicit. After Step 2's fold-in assessment delivers the agreed scope:

• Walk through the feature doc's Open questions section plus any open questions surfaced by fold-in candidates. Which need to resolve before coding? Which can stay open as known-unknowns?
• Confirm phase scope at chunk granularity. Step 2's agreed scope is the high-level frame; this step decomposes to specific phase rows + fold-in subsets the implementation will produce.
• Confirm composition mechanism per chunk — (i) subtractive (no new code), (ii) config-driven (existing behavior gains a knob), (iii) new behavior (route to /spf-create-behavior), (iv) behavior update with purpose change (route to /spf-update-behavior), (v) behavior refactor with preserved purpose (route to /refactor-behavior), (vi) media-layer / network-layer change (handle inline or defer per the downstream-skill-missing branch).
• Resolve open questions the implementation needs. Only resolve what the implementation forces; leave the rest as open questions in the doc.

Use AskUserQuestion for clear-cut choices (open-question resolutions, composition mechanism per chunk).

Step 4 — Map phases to implementation chunks

Per the agreed scope, decompose into discrete chunks. Each chunk is:

• Small enough to TDD individually — one test (or small test set), one implementation file change, one composition wiring tweak.
• Categorized by composition mechanism — drives Step 7's routing.
• Sequenced for least-risk order — primitives before behaviors; behaviors before composition wiring; composition wiring before integration tests.

Output of this step. A chunk list per the table shape:

Chunk	Mechanism	Downstream skill	Test target
Add bandwidthState audio sampling	Update existing	/spf-update-behavior (setupAudioBufferActors)	setup-buffer-actors.test.ts audio sampling assertion
Create switchAudioQuality behavior	New behavior	/spf-create-behavior	switch-audio-quality.test.ts (new)
Wire switchAudioQuality into composition	Composition	None	engine.test.ts composition assertion

Step 5 — Apply cross-cutting concern checks

Run the failure-mode catalog and the feature doc's Likely cross-cutting impact section against the chunk list. Each check fires when its signals are present:

• Conventions catalog application — per chunk, which conventions apply? (behaviors.md for behavior creation/update; signals.md for slot map design + multi-writer; reactors.md for FSM-driven behaviors; actors.md for actor creation/consumption; config.md for config surface).
• Multi-writer characterization — for any slot the implementation writes that another behavior already writes, three-axis check per conventions/signals.md.
• Per-type vs uniform-across-tracks — per conventions/behaviors.md, ensure per-type chunks follow the sibling-behaviors-plus-shared-helper pattern; uniform chunks compose against the aggregating resource.
• Composition-variant logic — variant-specific chunks go in variant factories, not the default factory.
• MSE codec-change implications — per /spf-document-feature's catalog, if the chunk touches buffer behavior and codecs change, surface the changeType() vs flushBuffer question explicitly.

Step 6 — TDD plan

For each chunk, name:

• The test — file path, test name, what it asserts.
• The implementation target — file path, behavior/actor/helper name.
• The composition wiring change — if any.
• Acceptance criterion — what does "done" look like for this chunk?

This output drives Step 7's per-chunk implementation loop.

The TDD plan is the seed of the feature doc's Verification section. Step 9 persists each chunk's test (file path + test name + assertion summary) into the feature doc — the TDD plan does not live only in chat. Name tests with assertion summaries suitable for the doc from the start, so Step 9 is a transcription pass rather than a re-articulation.

Step 7 — Implement (test-first per chunk; route to downstream skills)

Iterate per chunk:

1. Write the test first. Run it failing.
2. Branch by mechanism:
   • Subtractive / composition wiring — handle inline.
   • Config-driven — handle inline.
   • New behavior — route to /spf-create-behavior for this chunk.
   • Behavior update (purpose changing) — route to /spf-update-behavior.
   • Behavior refactor (purpose preserved) — route to /refactor-behavior.
   • Structural (split/merge) — route via /refactor-behavior's decomposition check.
   • Media-layer / network-layer — handle inline for now; future skills will own these.
3. Run the test passing.
4. Run composition tests to verify no regression.

Downstream skill missing — explicit handling. When a chunk routes to a downstream skill that doesn't exist yet (or is only a stub):

• Defer this chunk pending the downstream skill's full development — if the chunk isn't load-bearing for the implementation goal.
• Build the downstream skill inline now — pause the implementation, invoke /create-skill to build the missing discipline, resume the implementation after the new skill lands.
• Apply discipline ad-hoc with "extract later" flag — implement the chunk with explicit awareness that the discipline isn't yet codified; flag in commit message and surface for skill extraction later.

The user makes the call. Don't apply ad-hoc discipline silently.

Step 8 — Final-shape audit (per chunk + cumulative)

After each chunk:

• Test passes? Composition tests pass?
• Conventions adherence? Per-chunk check against the relevant conventions docs.
• No scope creep? Did the chunk stay within the agreed scope?

Cumulative audit after all chunks:

• Feature-doc grounding — does the implementation match what the doc said? Document drift surfaces here.
• Cross-cutting impacts honored? — every entry in the doc's Likely cross-cutting impact section either addressed or explicitly deferred?
• No silent ad-hoc downstream discipline? — if any chunk went the "apply discipline ad-hoc" path in Step 7, the extraction TODO is flagged.

Step 9 — Update feature doc as living artifact

The feature doc transitions from coarse → sketched (or technical → sketched) as code lands. Required updates:

• Frontmatter status — implemented once all phases land; partial if any phase landed but others haven't. Update definition per the rule below.
• Status block — reflect the implementation state, naming what shipped and what remains.
• Phases of complexity — phase rows that are now implemented may promote to implemented; partially-implemented rows note the partial state.
• What's not implemented — shrink to reflect what's now done.
• Implementation surface (required once any phase implementation lands) — populated with actual file paths, behavior names, state slots, helpers. See audio-playback.md for the canonical shape.
• Verification (required once any phase implementation lands) — this is the persisted TDD artifact. The Step 6 TDD plan lives here in the doc, not just in chat. Each chunk's test gets a line: test file path → test name → assertion summary. Add Sandbox entries where demos exist; add Out of scope / deferred sub-list for verification gaps (sandbox follow-ups, E2E coverage deferred elsewhere).
• Open questions — resolved-through-implementation entries moved to a Resolved during Phase N implementation sub-section (kept for traceability); new open questions surfaced by implementation experience added.
• Related features — if implementation revealed new cross-feature dependencies, add cross-refs.

definition advancement rule. Advance per the highest implementation depth across all phases:

• Any phase's Implementation surface + Verification sections populated with concrete exports / file paths / test names → sketched.
• Phases all still scope-and-constraints-only, no implementation → leave at technical.
• Phases still broadly sketched, many open questions → leave at coarse.

A feature with Phase 1 implemented but other phases still broadly sketched is sketched at the doc level — populated surface trumps unimplemented phases (which surface in Phases of complexity as not-yet-landed rows, not in the doc's overall depth).

This is not optional — the doc-as-living-artifact discipline is load-bearing for the registry staying current. Per the Status update skipped failure-mode entry.

Step 10 — Commit (with user confirmation)

After Step 8 audit is clean and Step 9 doc update lands:

1. Audit working-tree state. git status -s. Surface any pre-existing uncommitted work outside the implementation scope.
2. Propose a commit structure. Common shapes:
   • Per-chunk commits — one commit per chunk + a final doc-update commit. Highest atomicity; clearest review trail.
   • Per-phase commits — chunks bundled by phase; one commit per phase + doc update. Cleaner when chunks within a phase are tightly coupled.
   • Single feature-implementation commit + doc-update commit — small features (audio-abr per its doc) may fit one commit.
   • All-in-one — small enough that splitting adds no value.
3. Ask the user to confirm via AskUserQuestion.
4. On confirmation, run the commits. Use feat(spf) for behavior- adding work; refactor(spf) for behavior-refactoring chunks; docs(spf) for the feature-doc update; conventional-commit scopes per the git skill.
5. On decline or skip, stop. The user owns the commit boundary.

Output format

Propose Steps 1–6 outputs as structured reports before writing any code, in order:

1. Feature identification (Step 1's report — feature, composition target, sources, template implementations)
2. Destination architecture + fold-in assessment (Step 2's report — cluster/axis identification, destination sketch, candidate assessment + recommendations, value-curve check, agreed scope after AskUserQuestion confirmations)
3. Open questions + final scope (Step 3 — resolved open questions, final phase-and-fold-in scope at chunk granularity)
4. Chunk decomposition (Step 4 — chunk list with mechanism + downstream skill routing)
5. Cross-cutting concerns (Step 5)
6. TDD plan (Step 6 — per-chunk test + implementation targets)

After user confirmation, proceed to Step 7 per-chunk loop. Surface Steps 8–10 outputs after implementation.

Why this order

Same shape as /spf-document-feature and /spf-document-use-case. Steps 1–3 force framing before mechanical work. Step 4 commits to a concrete chunk list; Step 5 runs cross-cutting checks while context is fresh; Step 6 commits to TDD targets. Step 7 produces the artifact; Step 8 audits. Step 9 keeps the registry current. Step 10 hands the commit boundary to the user.

The novel disciplines compared to the document-* skills:

• Step 2's destination-architecture + fold-in assessment — implementation choices anchor the architecture more concretely than documentation choices do; framing the destination + fold-ins before scope-committing prevents local-optimal cuts that don't fit the cluster's larger shape.
• Chunk decomposition + per-chunk downstream-skill routing at Steps 4–7 — implementation work is inherently more granular than documentation work, and the chunk-level discipline is what keeps it from sprawling.

Why two discussion stages (not one)

This skill has two explicit conversational stages: Step 2 (fold-in assessment, with AskUserQuestion per candidate) and Step 3 (open questions + final scope). They're separated because they resolve different decisions:

• Step 2 decides which features / phases the implementation pass touches. Without this stage, scope-narrow choices that look reasonable in isolation ship implementations that get reworked when adjacent features land, or ship capabilities of low user-meaningful value.
• Step 3 decides the specifics within the agreed scope — open questions, composition mechanisms per chunk, phase-row-level cuts. Without this stage, the open-questions-skipped failure mode produces silent design decisions that lose the user's design intent.

Both stages use AskUserQuestion. Resolving them in chat — without the structured presentation — loses both the agreed scope and the agreed design decisions.

When this is the wrong skill

• You want to document a feature (not yet implemented) → /spf-document-feature.
• You want to implement a use-case composition (not a single feature) → /spf-implement-use-case. That skill consumes the use-case doc and routes per-constituent-feature back into this skill.
• You want to refactor an existing behavior without feature scope → /refactor-behavior.
• You want to split or merge behaviors → /refactor-behavior's decomposition check.
• You want to write an architectural design doc → design skill.
• You want to write an RFC → rfc skill.

How the failure-mode catalog grows

Same pattern as other SPF skills: when a new failure mode surfaces during use (most likely during Step 7 per-chunk implementation or Step 8 audit):

1. Add an entry to the Failure-mode catalog section above with the risk pattern and a worked-example citation.
2. If the failure-mode is downstream-skill-shaped (a recurring need surfaces in spf-create-behavior / spf-update-behavior), the downstream skill's catalog grows too.
3. If the failure-mode is doc-shape-shaped (the feature doc template / conventions don't capture something), /spf-document-feature's catalog or the conventions docs grow.

This skill is new; the catalog is expected to grow significantly as the first real implementations exercise it. The seeded entries capture patterns identified at skill-creation time from cross-skill failure-mode analysis — they're starting points, not endpoints.